Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data
Objective Epileptic seizures are neurological events that pose significant risks of physical injuries characterized by sudden, abnormal bursts of electrical activity in the brain, often leading to loss of consciousness and uncontrolled movements. Early seizure detection is essential for timely treat...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2024-11-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076241277185 |
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| author | Madiha Khalid Ali Raza Adnan Akhtar Furqan Rustam Julien Brito Ballester Carmen Lili Rodriguez Isabel de la Torre Díez Imran Ashraf |
| author_facet | Madiha Khalid Ali Raza Adnan Akhtar Furqan Rustam Julien Brito Ballester Carmen Lili Rodriguez Isabel de la Torre Díez Imran Ashraf |
| author_sort | Madiha Khalid |
| collection | DOAJ |
| description | Objective Epileptic seizures are neurological events that pose significant risks of physical injuries characterized by sudden, abnormal bursts of electrical activity in the brain, often leading to loss of consciousness and uncontrolled movements. Early seizure detection is essential for timely treatments and better patient outcomes. To address this critical issue, there is a need for an advanced artificial intelligence approach for the early detection of epileptic seizure disorder. Methods This study primarily focuses on designing a novel ensemble approach to perform early detection of epileptic seizure disease with high performance. A novel ensemble approach consisting of a fast, independent component analysis random forest (FIR) and prediction probability is proposed, which uses electroencephalography (EEG) data to investigate the efficacy of the proposed approach for early detection of epileptic seizures. The FIR model extracts independent components and class prediction probability features, creating a new feature set. The proposed model combined integrated component analysis (ICA) with predicting probability to enhance seizure recognition accuracy scores. Extensive experimental evaluations demonstrate that FIR assists machine learning models to obtain superior results compared to original features. Results The research gap is addressed using combined features to improve the performance of epileptic seizure detection compared to a single feature set. In particular, the ensemble model FIR with support vector machine (FIR + SVM) outperforms other methods, achieving an accuracy of 98.4% for epileptic seizure detection. Conclusions The proposed FIR has the potential for early diagnosis of epileptic seizures and can significantly help the medical industry with enhanced detection and timely interventions. |
| format | Article |
| id | doaj-art-a2bd883cae8349dea6369adaad9338ce |
| institution | OA Journals |
| issn | 2055-2076 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-a2bd883cae8349dea6369adaad9338ce2025-08-20T02:26:20ZengSAGE PublishingDigital Health2055-20762024-11-011010.1177/20552076241277185Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG dataMadiha Khalid0Ali Raza1Adnan Akhtar2Furqan Rustam3Julien Brito Ballester4Carmen Lili Rodriguez5Isabel de la Torre Díez6Imran Ashraf7 School of Computer Science and Engineering, Central South University, Changsha, Hunan, China Department of Software Engineering, University Of Lahore, Lahore, Pakistan Institute of Business Administration, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan School of Computer Science, , Dublin, Ireland Universidad de La Romana, La Romana, Republica Dominicana Universidade Internacional do Cuanza, Cuito, Angola Department of Signal Theory and Communications and Telematic Engineering, , Paseo de Belen Valladolid, Spain Department of Information and Communication Engineering, Yeungnam University, Gyeongsan South KoreaObjective Epileptic seizures are neurological events that pose significant risks of physical injuries characterized by sudden, abnormal bursts of electrical activity in the brain, often leading to loss of consciousness and uncontrolled movements. Early seizure detection is essential for timely treatments and better patient outcomes. To address this critical issue, there is a need for an advanced artificial intelligence approach for the early detection of epileptic seizure disorder. Methods This study primarily focuses on designing a novel ensemble approach to perform early detection of epileptic seizure disease with high performance. A novel ensemble approach consisting of a fast, independent component analysis random forest (FIR) and prediction probability is proposed, which uses electroencephalography (EEG) data to investigate the efficacy of the proposed approach for early detection of epileptic seizures. The FIR model extracts independent components and class prediction probability features, creating a new feature set. The proposed model combined integrated component analysis (ICA) with predicting probability to enhance seizure recognition accuracy scores. Extensive experimental evaluations demonstrate that FIR assists machine learning models to obtain superior results compared to original features. Results The research gap is addressed using combined features to improve the performance of epileptic seizure detection compared to a single feature set. In particular, the ensemble model FIR with support vector machine (FIR + SVM) outperforms other methods, achieving an accuracy of 98.4% for epileptic seizure detection. Conclusions The proposed FIR has the potential for early diagnosis of epileptic seizures and can significantly help the medical industry with enhanced detection and timely interventions.https://doi.org/10.1177/20552076241277185 |
| spellingShingle | Madiha Khalid Ali Raza Adnan Akhtar Furqan Rustam Julien Brito Ballester Carmen Lili Rodriguez Isabel de la Torre Díez Imran Ashraf Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data Digital Health |
| title | Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data |
| title_full | Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data |
| title_fullStr | Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data |
| title_full_unstemmed | Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data |
| title_short | Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data |
| title_sort | diagnosing epileptic seizures using combined features from independent components and prediction probability from eeg data |
| url | https://doi.org/10.1177/20552076241277185 |
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